Wealth bias and imbalance of opportunity need to be cured. That’s because they will appear in society as various side effects (IMF, 2016). Apparently, Innovations in transportation, communications, finance, manufacturing, IT, etc. as well as international agreements between nations have led to the most affluent period in human history. Yet, global income inequality has not yet been resolved. one-ninth of the world’s population (800 million people) is suffering from starvation (UN 2018) and Less than $1.9 a day, the absolute poverty rate is over 700 million, 10% of the world’s population. Also, If World Bank (2018) reflects the national poverty line ($5.5) in the middle income countries,46% of the world’s population still nearly half of the world’s population is absolute poor. This inequality is expected to continue after COVID-19. About 1,000 top billionaires have already recovered their previous highs in wealth recovery since COVID-19, but the world’s poorest are still recovering, which is expected to take more than a decade (WEF, 2021) and accelerate global imbalances. Accordingly, our team want to analyze the data with the research question. " How has income disparities changed throughout the years on the countries in the world?"
We tried to find the answer to our research question by tracking thoroughly how income of both country and continent level increased during the time frame of the data. We’ve checked how the growth of GDP per capita is showing up worldwide every year.
Countries such as United State have seen an increase in GDP PER CAPITA every year, while countries such as Brazil and India have seen little or no change.
This can also be seen briefly in comparison via Map. In regions such as Africa and Asia, there is little change in color from 20k to 0 on GDP INDEX, while in regions such as America and Europe, there is a change in color. furthermore, countries showed as a white on the map either didn’t have data or they were missing form the data.
We can see that inequality persists from map and plot data.
Data we used in project is WDI, masked from the World Development Indicators which is a compilation of relevant, high-quality, and internationally comparable statistics about global development and the fight against poverty. The database contains 1,400 time series indicators for 217 economies and more than 40 country groups, with data for many indicators going back more than 50 years.
But our indicator variables and the time spam was limed to from 1985 to 2019 and eight seven variables. as it can be seen on the table below, when it filtered in one year. but generally data contains 6475 observations and 7 variables.
## # A tibble: 25 x 7
## country year gdp_perc pop edu_spend iso3c continent
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr>
## 1 Afghanistan 2010 543. 29185507 3.48 AFG Asia
## 2 Albania 2010 4094. 2913021 NA ALB Europe
## 3 Algeria 2010 4479. 35977455 NA DZA Africa
## 4 American Samoa 2010 10271. 56079 NA ASM Oceania
## 5 Andorra 2010 40853. 84449 2.98 AND Europe
## 6 Angola 2010 3588. 23356246 3.42 AGO Africa
## 7 Antigua and Barbuda 2010 13049. 88028 NA ATG Americas
## 8 Argentina 2010 10386. 40788453 5.02 ARG Americas
## 9 Armenia 2010 3218. 2877319 3.25 ARM Asia
## 10 Aruba 2010 23513. 101669 6.93 ABW Americas
## # ... with 15 more rows
How has income disparities changed throughout 1985 to 2019 on the Map in the world Countries
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the plot below shows us how has Medium income increased unequally in the different continents throughout the years in the data.
This another line plot shows also same result when it comes a country level. the sampled countries in the graph witnessed unproportionally raise of their income during the time interval of the data.
Finally, we checked for sake of curiosity whether higher income per capita can be translated into higher government expenditure on education throughout the years. since high skilled labour is precondition for countries’ productivity and economic development. We looked at whether there is relationship between gdp per capita and education spending. The increase in the level of gdp per capita overtime is related to government spending on education as we can in the below scatter plot graph.